RESUMEN
OBJECTIVE: Wavelet-crosscorrelation analysis is a new application of wavelet analysis used to show the propagation of epileptiform discharges and to localize the corresponding lesions. We have shown previously that this analysis can help predict brain conditions statistically (Mizuno-Matsumoto et al. 2002). Our objective was to assess whether wavelet-crosscorrelation analysis reveals the initiation and propagation of epileptiform activity in human patients. METHODS: The data obtained from three patients with simple partial seizures (SPS) using whole-head magnetoencephalography (MEG) were analyzed by the wavelet-crosscorrelation method. Wavelet-crosscorrelation coefficients (WCC), the coherent structure of each possible pair of signals from 64 MEG channels forvarious periods, and the time lag (TL) in two related signals, were ascertained. RESULTS: We clearly demonstrated both localization of the irritative zone and propagation of the epileptiform discharges. CONCLUSIONS: Wavelet-crosscorrelation analysis can help reveal and visualize the dynamic changes of brain conditions. The method of this analysis can compensate for other existing methods for the analysis of MEG, electroencephalography (EEG) or Elecotrocorticography (ECoG). SIGNIFICANCE: Our proposed method suggests that revealing and visualizing the dynamic changes of brain conditions can help clinicians and even patients themselves better understand such conditions.
Asunto(s)
Corteza Cerebral/fisiopatología , Epilepsia/diagnóstico , Epilepsia/fisiopatología , Potenciales Evocados/fisiología , Magnetoencefalografía/métodos , Adulto , Algoritmos , Mapeo Encefálico/instrumentación , Mapeo Encefálico/métodos , Corteza Cerebral/anatomía & histología , Femenino , Humanos , Magnetoencefalografía/instrumentación , Masculino , Valor Predictivo de las Pruebas , Reproducibilidad de los Resultados , Procesamiento de Señales Asistido por Computador/instrumentación , Estadística como Asunto/instrumentación , Estadística como Asunto/métodos , Interfaz Usuario-ComputadorRESUMEN
OBJECTIVES: In our research on brain function analysis, users require two different simultaneous types of processing: interactive processing to a specific part of data and high-performance batch processing to an entire dataset. The difference between these two types of processing is in whether or not the analysis is for data in the region of interest (ROI). In this study, we propose a Grid portal that has a mechanism to freely assign computing resources to the users on a Grid environment according to the users' two different types of processing requirements. METHODS: We constructed a Grid portal which integrates interactive processing and batch processing by the following two mechanisms. First, a job steering mechanism controls job execution based on user-tagged priority among organizations with heterogeneous computing resources. Interactive jobs are processed in preference to batch jobs by this mechanism. Second, a priority-based result delivery mechanism that administrates a rank of data significance. RESULTS: The portal ensures a turn-around time of interactive processing by the priority-based job controlling mechanism, and provides the users with quality of services (QoS) for interactive processing. The users can access the analysis results of interactive jobs in preference to the analysis results of batch jobs. The Grid portal has also achieved high-performance computation of MEG analysis with batch processing on the Grid environment. CONCLUSION: The priority-based job controlling mechanism has been realized to freely assign computing resources to the users' requirements. Furthermore the achievement of high-performance computation contributes greatly to the overall progress of brain science. The portal has thus made it possible for the users to flexibly include the large computational power in what they want to analyze.